Radical hysterectomy teaching module with a three‐dimensional digital model of the female pelvis
Bibliographic record
Abstract
Performing radical hysterectomy requires thorough knowledge of pelvic anatomy, which is complex and difficult to visualize. The goal of this project is to create a teaching tool to enable surgeons worldwide to improve their understanding of pelvic anatomy, leading to less invasive procedures and better patient outcomes. The pelvic model was created using AMIRA, a 3D segmentation and surface‐rendering program. Anatomical areas of interest were labeled on consecutive 2D slices from the Visible Human Project, which were then reassembled to form a 3D model and incorporated into animated videos. An animated instructional program based on radical hysterectomy was created, which includes rotational viewing, real‐life video, clinical narration, correlated imaging, labeling and a glossary. The glossary standardizes anatomic and surgical terminology to help overcome language barriers when training doctors whose native language is not English. Due to the detail, ease of use and versatility, this tool will be an excellent resource for surgeons training to perform radical hysterectomy, allowing for better patient outcomes especially in developing countries where this technology was not previously available. Grant Funding Source : Ontario Graduate Scholarship
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".